Croitor Sava A, Sima D M, Martinez-Bisbal M C, Celda B, Van Huffel S
Department of Electrical Engineering (ESAT-SCD) - Biomed, Katholieke Universiteit Leuven, Belgium.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3658-61. doi: 10.1109/IEMBS.2010.5627436.
Given High Resolution Magic Angle Spinning (HR-MAS) signals from several glioblastoma tumor subjects, the goal is to differentiate between tumor tissue types by separating the different sources that contribute to the profile of each spectrum. Blind source separation techniques are applied for obtaining characteristic profiles for necrosis, high cellular tumor and border tumor tissue, and providing the contribution (abundance) of each tumor tissue to the profile of the spectra. The problem is formulated as a non-negative source separation problem. We illustrate the effectiveness of the proposed methods and we analyze to which extent the dimension of the input space could influence the performance by comparing the results on the full magnitude signals and on dimensionally reduced spaces.
给定来自多个胶质母细胞瘤肿瘤受试者的高分辨率魔角旋转（HR-MAS）信号，目标是通过分离对每个光谱特征有贡献的不同来源，来区分肿瘤组织类型。应用盲源分离技术来获取坏死、高细胞性肿瘤和肿瘤边缘组织的特征谱，并提供每种肿瘤组织对光谱特征的贡献（丰度）。该问题被表述为一个非负源分离问题。我们通过比较全幅度信号和降维空间上的结果，说明了所提方法的有效性，并分析了输入空间的维度在多大程度上会影响性能。
